Arten von räumlichen Daten:
Das R-paket ggmap wird im folgenden genutzt um verschiedene Kartentypen darzustellen.
Mit qmap kann man eine schnelle Karte erzeugen.
A road map is one of the most widely used map types.
install.packages("ggmap")
librarylibrary(ggmap)
qmap("Mannheim")
BBT <- qmap("Berlin Brandenburger Tor")
BBT
qmap("Germany")
qmap("Germany", zoom = 6)
?qmap
Different components in the help
Extract from the help file on qmap:
This examples can be directly copy-pasted to the console
qmap("baylor university")
qmap("baylor university", zoom = 14)
# and so on
qmap("Mannheim", zoom = 12)
qmap('Mannheim', zoom = 13)
qmap('Mannheim', zoom = 20)
qmap('Mannheim', zoom = 14, source="osm")
qmap('Mannheim', zoom = 14, source="osm",color="bw")
qmap('Mannheim', zoom = 14, maptype="satellite")
qmap('Mannheim', zoom = 21, maptype="hybrid")
qmap("Mannheim", zoom = 14, maptype="hybrid")
Physical maps illustrate the physical features of an area, such as the mountains, rivers and lakes. Colors are used to show relief differences in land elevations.
qmap('Schriesheim', zoom = 14,
maptype="terrain")
Source: Design faves
qmap('Mannheim', zoom = 14,
maptype="watercolor",source="stamen")
qmap('Mannheim', zoom = 14,
maptype="toner",source="stamen")
qmap('Mannheim', zoom = 14,
maptype="toner-lite",source="stamen")
qmap('Mannheim', zoom = 14,
maptype="toner-hybrid",source="stamen")
qmap('Mannheim', zoom = 14,
maptype="terrain-lines",source="stamen")
These high-contrast B+W (black and white) maps are featured in our Dotspotting project. They are perfect for data mashups and exploring river meanders and coastal zones.
Source: http://maps.stamen.com/
<- is an assignment operator which can be used to create an objectMA_map <- qmap('Mannheim',
zoom = 14,
maptype="toner",
source="stamen")
Geocoding (…) uses a description of a location, most typically a postal address or place name, to find geographic coordinates from spatial reference data …
library(ggmap)
geocode("Mannheim Wasserturm",source="google")
| lon | lat |
|---|---|
| 34.79565 | 32.1221 |
| cities | lon | lat |
|---|---|---|
| Hamburg | 9.993682 | 53.55108 |
| Koeln | 6.960279 | 50.93753 |
| Dresden | 13.737262 | 51.05041 |
| Muenchen | 11.581981 | 48.13513 |
Reverse geocoding is the process of back (reverse) coding of a point location (latitude, longitude) to a readable address or place name. This permits the identification of nearby street addresses, places, and/or areal subdivisions such as neighbourhoods, county, state, or country.
Source: Wikipedia
revgeocode(c(48,8))
## [1] "Unnamed Road, Somalia"
mapdist("Q1, 4 Mannheim","B2, 1 Mannheim")
mapdist("Q1, 4 Mannheim","B2, 1 Mannheim",mode="walking")
mapdist("Q1, 4 Mannheim","B2, 1 Mannheim",mode="bicycling")
What you should know:
Homework:
And now some more advanced stuff….
POI1 <- geocode("B2, 1 Mannheim",source="google")
POI2 <- geocode("Hbf Mannheim",source="google")
POI3 <- geocode("Wasserturm Mannheim",source="google")
ListPOI <-rbind(POI1,POI2,POI3)
POI1;POI2;POI3
## lon lat
## 1 8.462844 49.48569
## lon lat
## 1 8.469879 49.47972
## lon lat
## 1 8.466039 49.48746
MA_map +
geom_point(aes(x = lon, y = lat),
data = ListPOI)
MA_map +
geom_point(aes(x = lon, y = lat),col="red",
data = ListPOI)
ListPOI$color <- c("A","B","C")
MA_map +
geom_point(aes(x = lon, y = lat,col=color),
data = ListPOI)
ListPOI$size <- c(10,20,30)
MA_map +
geom_point(aes(x = lon, y = lat,col=color,size=size),
data = ListPOI)
from <- "Mannheim Hbf"
to <- "Mannheim B2 , 1"
route_df <- route(from, to, structure = "route")
qmap("Mannheim Hbf", zoom = 14) +
geom_path(
aes(x = lon, y = lat), colour = "red", size = 1.5,
data = route_df, lineend = "round"
)
More about adding points
ggmap: Spatial Visualization with ggplot2
by David Kahle and Hadley Wickham